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Eliana Lima da Fonseca Department of Geography Universidade Federal do Rio Grande do Sul

Laboratório de. Geotecnologias Aplicadas. BIOMASS MONITORING Aboveground biomass quantification for the natural grasslands in the Pampa biome using remotely-sensed images. Eliana Lima da Fonseca Department of Geography Universidade Federal do Rio Grande do Sul eliana.fonseca@ufrgs.br.

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Eliana Lima da Fonseca Department of Geography Universidade Federal do Rio Grande do Sul

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  1. Laboratório de Geotecnologias Aplicadas BIOMASS MONITORINGAboveground biomass quantification for the natural grasslands in the Pampa biome using remotely-sensed images Eliana Lima da Fonseca Department of Geography Universidade Federal do Rio Grande do Sul eliana.fonseca@ufrgs.br

  2. Pampa biome

  3. ABOVEGROUND BIOMASS QUANTIFICATION FOR THE NATURAL GRASSLANDS IN THE PAMPA BIOME USING REMOTELY-SENSED IMAGES Study area: “Environment Protect Area of Ibirapuitã” , which is a region with 320,000 hectares. Satellite images (NDVI – Spot Vegetation) Image DevCoCast file Field data (samples location) Shapefile Field data (aboveground biomass) Excel file NDVI values collected over samples Satellite images (NDVI – Spot Vegetation) Image DevCoCast file Regression model Biomass (kg.ha-1) = a + b*NDVI Biomass map

  4. Study area: “Environment Protect Area of Ibirapuitã” , which is a region with 320,000 hectares.

  5. NDVI time series over the Environment Protect Area of Ibirapuitã for the year 2002.

  6. Graph with the NDVI values collected over the sample area where measurements in situ were made

  7. Mathematical relationship between NDVI and aboveground biomass for year 2002 and the ILWIS command to calculate the set of biomass map over the Ibirapuita_2002” map list

  8. Aboveground biomass maps over the Environment Protect Area of Ibirapuitã for the year 2002.

  9. Verification of the results Comparison between calculate and measure aboveground biomass Residual analysis Considerations about the results For this kind of vegetation (natural grasslands) is not expected great values for the coefficient of determination, because these is non-homogeneous area, since the Pampa biome support very high levels of biodiversity. How to improve these results? The results of this model can be improved with a bigger dataset of “in situ” measurements in different plots and a longer time series for analysis.

  10. Conclusions • The Spot-Vegetation sensor allows to make good estimates for the grassland aboveground biomass using the NDVI images, since have an equation (mathematic model) to convert the satellite images in biomass. • This kind of information is useful to monitoring the Pampa biome, and it is necessary in order to preserve the natural vegetation in association with economic exploration done by the traditional people.

  11. Conclusions • A model calibration for each kind of vegetation cover, also considering the local weather, allows making more realistic models, which are more useful at local conditions and at regional scale, when it is compared with global scale models. • To develop a global model to estimate the aboveground biomass some generalizations are made, like consider the Brazilian Cerrado as an African Savanna. • These generalizations are necessary for built a global scale model, but it can be an obstacle to apply the results in order to local planning.

  12. Laboratório de Geotecnologias Aplicadas Acknowledgments for Brazilian Staff Charles Tebaldi2; Adriana Ferreira da Costa Vargas3; Vicente Celestino Pires Silveira4 2 Student at Bachelor in Geography Course - Universidade Federal do Rio Grande do Sul (UFRGS) – Brazil 3 Agronomist at Fundacao Maronna - Brazil 4 Professor at Centre of Rural Science - Universidade Federal de Santa Maria (UFSM) - Brazil Special thanks for ITC Staff !!

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